most citedLiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

2 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2026

Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation

Yuqing Huang, Guotian Zeng, Zhenqiao Yuan +4

Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To…

cs.CV20231 cited

RTrack: Accelerating Convergence for Visual Object Tracking via Pseudo-Boxes Exploration

Guotian Zeng, Bi Zeng, Hong Zhang +2

Single object tracking (SOT) heavily relies on the representation of the target object as a bounding box. However, due to the potential deformation and rotation experienced by the…

cs.CV20232 cited

LiteTrack: Layer Pruning with Asynchronous Feature Extraction for Lightweight and Efficient Visual Tracking

Qingmao Wei, Bi Zeng, Jianqi Liu +2

The recent advancements in transformer-based visual trackers have led to significant progress, attributed to their strong modeling capabilities. However, as performance improves, r…

cs.CV20231 cited

Towards Efficient Training with Negative Samples in Visual Tracking

Qingmao Wei, Bi Zeng, Guotian Zeng

Current state-of-the-art (SOTA) methods in visual object tracking often require extensive computational resources and vast amounts of training data, leading to a risk of overfittin…

cs.CV20232 cited

Efficient Training for Visual Tracking with Deformable Transformer

Qingmao Wei, Guotian Zeng, Bi Zeng

Recent Transformer-based visual tracking models have showcased superior performance. Nevertheless, prior works have been resource-intensive, requiring prolonged GPU training hours…